Extract eval code from GPTQ for more general usage

Summary: This commit extracts all the eval code from GPTQ.py.
This is the first step towards having a general eval framework
in torchao. The eventual goal is to use lm_eval to produce
reproducible benchmarks for the quantization APIs in torchao
that we can showcase on the main README. This will have the
added benefit of allowing us to add (possibly nightly)
regression test suites for important models.

Test Plan:

```
2024-05-24:14:50:32,647 INFO     [task.py:395] Building contexts for wikitext on rank 0...
100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 1132.98it/s]
2024-05-24:14:50:32,648 INFO     [evaluator.py:362] Running loglikelihood_rolling requests
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:51<00:00, 51.39s/it]
wikitext: {'word_perplexity,none': 7.877762491958485, 'word_perplexity_stderr,none': 'N/A', 'byte_perplexity,none': 1.488984329919892, 'byte_perplexity_stderr,none': 'N/A', 'bits_per_byte,none': 0.5743285710685551, 'bits_per_byte_stderr,none': 'N/A', 'alias': 'wikitext'}
.
----------------------------------------------------------------------
Ran 1 test in 858.105s

OK
```

python test/quantization/test_quant_api.py -k test_8da4w_gptq_quantizer

test/quantization/test_quant_api.py

@@ -172,9 +172,14 @@ def test_8da4w_quantizer(self):
         assert isinstance(m.linear2, Int8DynActInt4WeightLinear)
         m(*example_inputs)
 
+    # TODO: save model weights as artifacts and re-enable in CI
+    # For now, to run this test, you will need to download the weights from HF
+    # and run this script to convert them:
+    # https://github.com/pytorch-labs/gpt-fast/blob/6253c6bb054e658d67566150f87329b87815ae63/scripts/convert_hf_checkpoint.py
     @unittest.skip("skipping until we get checkpoints for gpt-fast")
     def test_8da4w_gptq_quantizer(self):
-        from torchao.quantization.GPTQ import Int8DynActInt4WeightGPTQQuantizer, InputRecorder, TransformerEvalWrapper
+        from torchao.quantization.GPTQ import Int8DynActInt4WeightGPTQQuantizer
+        from torchao.quantization._eval import InputRecorder, TransformerEvalWrapper
         # should be similar to TorchCompileDynamicQuantizer
         precision = torch.bfloat16
         device = "cpu"
@@ -236,7 +241,7 @@ def test_8da4w_gptq_quantizer(self):
     @unittest.skipIf(not TORCH_VERSION_AFTER_2_4, "skipping when torch verion is 2.4 or lower")
     def test_8da4w_quantizer_eval(self):
         from torchao.quantization.quant_api import Int8DynActInt4WeightQuantizer
-        from torchao.quantization.GPTQ import TransformerEvalWrapper
+        from torchao.quantization._eval import TransformerEvalWrapper
 
         precision = torch.bfloat16
         device = "cpu"
@@ -270,7 +275,8 @@ def test_8da4w_quantizer_eval(self):
 
     @unittest.skip("skipping until we get checkpoints for gpt-fast")
     def test_gptq_quantizer_int4wo(self):
-        from torchao.quantization.GPTQ import Int4WeightOnlyGPTQQuantizer, InputRecorder, TransformerEvalWrapper
+        from torchao.quantization.GPTQ import Int4WeightOnlyGPTQQuantizer
+        from torchao.quantization._eval import InputRecorder, TransformerEvalWrapper
         precision = torch.bfloat16
         device = "cuda"
         checkpoint_path = Path("../gpt-fast/checkpoints/meta-llama/Llama-2-7b-chat-hf/model.pth")
@@ -329,7 +335,8 @@ def test_gptq_quantizer_int4wo(self):
 
     @unittest.skip("skipping until we get checkpoints for gpt-fast")
     def test_quantizer_int4wo(self):
-        from torchao.quantization.GPTQ import Int4WeightOnlyQuantizer, TransformerEvalWrapper
+        from torchao.quantization.GPTQ import Int4WeightOnlyQuantizer
+        from torchao.quantization._eval import TransformerEvalWrapper
         precision = torch.bfloat16
         device = "cuda"
         checkpoint_path = Path("../gpt-fast/checkpoints/meta-llama/Llama-2-7b-chat-hf/model.pth")
@@ -364,7 +371,7 @@ def test_quantizer_int4wo(self):
 
     @unittest.skip("skipping until we get checkpoints for gpt-fast")
     def test_eval_wrapper(self):
-        from torchao.quantization.GPTQ import TransformerEvalWrapper
+        from torchao.quantization._eval import TransformerEvalWrapper
         precision = torch.bfloat16
         device = "cuda"
         checkpoint_path = Path("../gpt-fast/checkpoints/meta-llama/Llama-2-7b-chat-hf/model.pth")